Learn about your AI options, who are the players, and what are the key technologies. Hear from world-class machine learning experts at Fortune 500 businesses that are developing innovative enterprise applications today.

This half day seminar provides a comprehensive introduction to attendees on the entire machine learning (ML) industry, including the different players, options, and technologies. A particular focus will be on deep learning (DL) and reinforcement learning
(RL) to enable attendees to gain in-depth concepts of DL, which revolutionizes data science tasks such as image recognition, speech analysis or time series prediction as well as RL.

Participants will:

Gain a thorough overview of today’s ML, DL and RL market,

Learn when to use machine learning and when to use deep learning and reinforcement learning,

Gain knowledge about how and where to get and use the right type of data for different applications,

Learn where in your organization you can find the best type of applications,

Learn what type of approach your company should use in terms of building your own team and/or working with third party service and software providers, and

What options are now available in the cloud.

Attendees will also gain a deep sense of understanding of what steps to take next and will be well equipped to accelerate their internal business AI initiatives.

1:15 State-of-the-Practice of Machine Learning in the Enterprise

Jan Kanty Milczek, Senior Data Scientist, deepsense.ai

Machine learning is often described as the technology of the future, but it is also the technology of the present. In this talk, we describe the applications of both simple and complicated algorithms to earn money or solve problems here and now. This
comprehensive enterprise-level introduction to machine learning and deep learning technology includes approaches such as:

How should you go about creating value from customer data from multiple sources? A common approach is to use manual feature engineering, but can we do better? Especially in the currently highly diversified and multidimensional world of possible customer
data, the automated approach to analyzing interactions between different data sources is key. In this presentation, we will use our recent case study to answer questions like:

How can deep learning be used to extract valuable customer information from raw data?

This strategic overview of the deep learning market is provided by deepsense.ai’s experts and professionals from large enterprises who have deployed deep learning applications at their company. Hear from world class heads of data science
and innovation departments for major U.S. companies. Learn how the largest organizations use machine learning techniques, how deep learning is disrupting their industries, and critical lessons learned in deploying enterprise class machine
learning applications. After the panel, everyone is invited to take part in the discussion and ask questions about practical applications of AI technology.

The panelists will address:

How to know if your business needs machine learning?

How to construct an effective plan for a successful machine learning deployment project?

What are new business sectors where deep learning solutions are not widely adopted yet, but which have potential?

What would you recognize as the most promising trends and technologies for the future?

Healthcare is facing problems on multiple axes, with low availability of expert care in developing countries and increasing demand for it in the aging population of the first world. Examples are provided showing healthcare applications where machine
learning either matches or surpasses human experts. Possible improvements are also explored that AI can bring into the healthcare field while considering the risks and reservations that have prevented it from being the industry standard.

Attendees will learn:

How to evaluate the accuracy of a machine learning model against medical expert diagnoses?

Cybersecurity is a major area of interest in today’s internet, and while the threats are no longer as widely discussed as they used to be, they are arguably more dangerous than ever. We show how the constant arms race between security experts
and malicious actors is a real-life example of a GAN – a generative adversarial network. We then talk about the ways in which the former may gain advantage. Examples include AI-powered sandboxing and analyses of Big Data unavailable
to the attackers.

Attendees will learn:

How machine learning can help with malware research?

How image recognition can classify malware?

The state of the tech

The state of the technology and state of the practice in AI and cybersecurity today